Fix LinkNeighborLoader producing double-sized edge_label_time for homogeneous graphs

CHANGELOG.md

@@ -77,6 +77,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Fixed `edge_label_time` computation in `LinkNeighborLoader` for homogeneous graphs ([#7807](https://github.com/pyg-team/pytorch_geometric/pull/7807))
 - Fixed `edge_label_index` computation in `LinkNeighborLoader` for the homogeneous+`disjoint` mode ([#7791](https://github.com/pyg-team/pytorch_geometric/pull/7791))
 - Fixed `CaptumExplainer` for `binary_classification` tasks ([#7787](https://github.com/pyg-team/pytorch_geometric/pull/7787))
 - Warn user when using the `training` flag in `to_hetero` modules ([#7772](https://github.com/pyg-team/pytorch_geometric/pull/7772))

test/loader/test_link_neighbor_loader.py

@@ -204,6 +204,34 @@ def test_link_neighbor_loader_edge_label():
         assert torch.all(batch.edge_label[10:] == 0)
 
 
+@withPackage("pyg_lib")
+def test_temporal_homo_link_neighbor_loader():
+    data = Data(
+        x=torch.randn(10, 5),
+        edge_index=torch.randint(0, 10, (2, 1234)),
+        edge_time=torch.arange(1234),
+        edge_label=torch.ones(1234),
+    )
+
+    batch_size = 1
+    loader = LinkNeighborLoader(
+        data,
+        num_neighbors=[-1],
+        time_attr="edge_time",
+        edge_label_time=data.edge_time,
+        batch_size=batch_size,
+        shuffle=True,
+    )
+
+    for sample in loader:
+        assert sample.edge_label_index.size() == (2, batch_size)
+        assert sample.edge_label_time.size() == (batch_size, )
+        assert sample.edge_label.size() == (batch_size, )
+        assert torch.all(
+            sample.edge_time <= sample.edge_label_time
+        ), "The target time should be later than all timestamps in the subgraph"
+
+
 @withPackage('pyg_lib')
 def test_temporal_hetero_link_neighbor_loader():
     data = HeteroData()

torch_geometric/sampler/neighbor_sampler.py

@@ -576,7 +576,7 @@ def edge_sample(
             else:
                 edge_label_index = inverse_seed.view(2, -1)
 
-            out.metadata = (input_id, edge_label_index, edge_label, seed_time)
+            out.metadata = (input_id, edge_label_index, edge_label, src_time)
 
         elif neg_sampling.is_triplet():
             if disjoint: